Towards An Autonomous Agent that Provides Automated Feedback on Students' Negotiation Skills

Although negotiation is an integral part of daily life, most people are unskilled negotiators. To improve one's skill set, a range of costly options including self-study guides, courses, and training programs are offered by various companies and educational institutions. For those who can't afford costly training options, virtual role playing agents offer a low-cost alternative. To be effective, these systems must allow students to engage in experiential learning exercises and provide personalized feedback on the learner's performance. In this paper, we show how a number of negotiation principles can be formalized and quantified. We then establish the pedagogical relevance of several automatic metrics, and show that these metrics are significantly correlated with negotiation outcomes in a human-agent negotiation. This illustrates the realism and helps to validate these principles. It also shows the potential of technology being used to quantify feedback that is traditionally provided through more qualitative approaches. The metrics we describe can provide students with personalized feedback on the errors they make in a negotiation exercise and thereby support guided experiential learning.